Attempts to investigate morbidity and mortality from the perspective of basic science suggest models of human disease processes complementary to those used by clinical scientists. Such models view diseases as resulting from two related phenomena: a) an intrinsic "wearing-out" or aging of the individual and b) manifest clinico-pathologic events -- our classical notion of diseases. Clinical scientists focus on b) because contemporary medical treatment is primarily therapeutic and response oriented to specific disease entities and seldom to profoundly changing the "age" of the individual. Generally, many of the important parameters of the physiological processes underlying the increased probability of pathological events are unobserved. In this study, a number of methodologic innovations are proposed to exploit new models of disease as partially observed Markov processes. Mixed continuous-discrete disease state models arise from the recognition that discretization of continuously varying distributed parameter systems and processes to measurements at a given time and to events is a necessary that can be carried too far. It is recognized that clinical scientists are well aware of the limitations of the discrete labels that they use and that patients are unique and their characterization transcends the labels used and also simple mathematical and statistical models. Patient uniqueness can be modeled in appropriately high dimensional mixed mode processes, and computations can be carried out using such models. The principal problem to be faced is that of embedding the theoretical constructs into a paradigm which will permit the evolution of more effective intervention by putting observations of pathologic events and processes into the proper perspective.